BENCHmark January 2008

Statistically Speaking....

When the National Lottery was first introduced to the UK a decade
ago, many were excited at what seemed like a very real chance of
instant riches. Statisticians pointed out that we were 175 times
more likely to be murdered in the street than to win the lottery.
One even went so far as to point out that you are five times more
likely to get knocked down by a bus on your way to get a ticket
than you are to win!

Facts such as these seem to be counter-intuitive, and perhaps they
illustrate that we are not very good at understanding uncertainty
and risk.

As engineers, we are traditionally taught to find the answer to a
specific problem. But we all know that the real world is not
deterministic, and that there is a degree of uncertainty associated
with material properties, loads, boundary conditions, geometrical
dimensions to name but a few.

In the past, in the world of engineering analysis, our difficulties
in comprehending such complexities have been compounded by the
restrictions imposed by the available computing power. This has
limited the extent to which statistical approaches could be applied
in a routine manner to a wide variety of applications.

Within the NAFEMS community, the recently formed Stochastics Working Group
essentially comprises a group of campaigners for a new way of
working: to encourage us all to look at the world in a more
realistic, and statistical, manner. The lack of available computing
power is not as much of a barrier as it once was, and the tools to
allow us to exploit statistical techniques are emerging and
maturing.

There is an argument to say that we don’t need to change what
we do, that everything is fine as it is, and that the various
safety factors inherent in most procedures adequately account for
uncertainties.

Certainly, if we are to embrace these methods, we will need to
develop entirely new ways of working, and of thinking. For example,
we would need to move away from the way in which we tend to compare
an analysis with a physical test and see if we get agreement, and
to start to think in terms of a "cloud" of points (or a
distribution) of results for both test and analysis.

If we can successfully adapt in this way, then there are further
clear advantages in making better and more reliable use of
optimisation, and also in being able to do a better job of
"designing to cost", both of which are discussed within this
issue.

Tim Morris, Chief Executive,

January 2008

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